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Automatic Run-time Adaptation in Virtual Execution Environments
Ananth I. Sundararaj
Advisor: Peter A. Dinda
Prescience Lab
Department of Computer Science
Northwestern University
http://virtuoso.cs.northwestern.edu
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Aim
Grid Computing
New Paradigm
Traditional Paradigm
Deliver arbitrary amounts of computational power to perform distributed and parallel computations
Problem1:
Grid Computing using virtual machines
Problem2:
Solution
How to leverage them?
Virtual Machines What are they?
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Resource multiplexing using OS level mechanism
Complexity from resource user’s perspective
Complexity from resource owner’s perspective
Virtual Machine Grid Computing
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Virtual Machines
Virtual machine monitors (VMMs)
•Raw machine is the abstraction
•VM represented by a single image
•VMware GSX Server
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The Simplified Virtuoso Model
Orders a raw machine
User
Specific hardware and performance
Basic software installation available
User’s LAN
VM
Virtual networking ties the machine back to user’s home network
Virtuoso continuously monitors and adapts
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User’s friendlyLAN
Foreign hostile LAN
Virtual Machine
VNET: A bridge with long wires
Host
Proxy
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Virtual NetworksVM traffic going out on foreign LAN
IP network
A machine is suddenly plugged into a foreign network. What happens?
• Does it get an IP address?• Is it a routeable address?• Does firewall let its traffic through? To any port?
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Measurement and Inference
Application (VTTIF)• Topology
• Traffic load
Underlying network layer Physical hosts
Virtual network layerVNET daemons
Application layerVM layer
Host and VM • Size and compute capacities
• Size and compute demands
• Topology
• Bandwidth
• Latency
Underlying network
[Gupta et al. LNCS 05]
[Gupta et al. In submission]
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Adaptation Mechanisms
Resource reservation• Network
• CPU
Resource reservationPhysical hosts
Topology changesVNET daemons
VM MigrationVM layer
Topology changes • Overlay links
• Overlay forwarding rules
VM Migration• Third party migration schemes
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[Sundararaj et al. LCR 04, HPDC 05]
[Lange et al. HPDC 05]
[Lin et al. GRID 2004]
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Generic Adaptation Problem In Virtual Execution Environments
• Goal: – VMs to Hosts mapping
– Path to each 4-tuple
– Meeting all demands within constraints
– Such that• Sum of residual bottleneck bandwidth over
each mapped path is maximized
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Optimizing Objective functions
• Many possibilities
• Maximizing sum of residual bottleneck bandwidths over each mapped path– Intuition:
• Leave the most room for application to increase performance
• Minimizing the residual bottleneck capacity– Intuition:
• Increase room for other applications to enter system
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Claim
• Wide spectrum of possibilities– Adaptation transparent to application– Application directed adaptation
• Claim– Adaptation using a single metric for a wide range
of applications is possible and feasible
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• For More Information– Prescience Lab (Northwestern University)
• http://plab.cs.northwestern.edu
– Virtuoso: Resource Management and Prediction for Distributed Computing using Virtual Machines
• http://virtuoso.cs.northwestern.edu
• VNET is publicly available from• http://virtuoso.cs.northwestern.edu